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188 results for “Big data”
Raw Laser Profilometer Data of Plan de Platanos, Big Piute and Water Man Hill Faults
<p>Raw laser profilometer data<strong> (Elevation matrices)</strong> for: </p> <ol> <li>Plan de los Platanos Fault (<strong>PP</strong>), Autlan de Navarro, Jalisco, Mexico---> Andesite (<strong>and</strong>)</li> <li>Big Piute Low Angle Normal Fault (<strong>BP</strong>), Mojave, Southern California--->quartzite (<strong>qtz</strong>)</li> <li>Waterman Hills Detachment Fault (<strong>WH</strong>), Mojave, Southern California---> mylonitized metasedimentary (<strong>mms</strong>)</li> </ol> <p><strong>Instrument:</strong> Taylor Hobson TALYSCAN 150 profilometer using a no-contact laser gauge</p> <p><strong>Vertical Resolution:</strong> 252nm</p> <p><strong>Traversing speed:</strong> 2mms-1</p> <p><strong>Horizontal Spacing:</strong> 5 µm (except for WH-03a1, which is 4 µm instead)</p> <p><strong>Unit of length</strong>: meters (m)</p> <p> </p> <p> </p>
Data for: Predicting habitat suitability for Townsend's big-eared bats across California in relation to climate change
<p>Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend's big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions.</p> <p>Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species' distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts.</p> <p>Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species' responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions.</p>
Data from: Using network science to evaluate vulnerability of landslides on Big Sur Coast, California, USA
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Data for: Predicting habitat suitability for Townsend’s big-eared bats across California in relation to climate change
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Data from: Not afraid of the Big Bad Wolf: calls from large predators do not silence mesopredators
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Data from: Effect of the combination floral and diet resources on development of big-eyed bug Geocoris ochropterus (Fieber) (Hemiptera: Geocoridae) at different temperature
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Big Spirit Lake Monitoring Buoy: High Frequency Oxygen, Temperature, Water Quality and Weather Data 2020-2021
High frequency weather and limnological data from the Big Spirit Lake monitoring buoy, Iowa, USA, from 2020 - 2021. The Big Spirit Lake (Global Lake Ecological Observatory Network—GLEON) instrumented monitoring buoy is operated by the Iowa Lakeside Laboratory (https://iowalakesidelab.org/). The buoy is equipped with a thermistor chain and dissolved oxygen probes the measure every meter from 1m to 6m in depth, a multi-parameter sonde at 1 meter depth, as well as a weather station and wave sensor (added in 2021) situated on the buoy. All data in this data package have been QAQC'd to remove obviously errant readings, highly suspicious readings, and artifacts of buoy maintenance. Because of this, as well as sensor errors and failure, not all data is available at every point each year. Measured parameters in the data set include water temperature, dissolved oxygen concentration, and dissolved oxygen saturation (measured every meter from 1m to 6m). Other measured parameters include pH, specific conductivity, chlorophyll, and blue-green algae in the surface water, as well as weather station data and wave sensor data. Sampling Frequency: Parameters are measured at 10 minute intervals, and most years include measurements from mid/late May through October.
Clinical trial generalizability assessment in the big data era: a review
<p><span><span><span>Clinical studies, especially randomized controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long-standing concern when applying trial results to real-world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic scoping review to understand the practice of generalizability assessment. We identified 187 relevant papers and systematically organized these studies in a taxonomy with three dimensions: (1) data availability (i.e., before or after trial [<i>a priori</i> vs <i>a posteriori</i> generalizability]), (2) result outputs (i.e., score vs non-score), and (3) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but less than 30% of studies reported positive generalizability results. As <i>a priori</i> generalizability can be assessed using only study design information (primarily eligibility criteria), it gives investigators a golden opportunity to adjust the study design before the trial starts. Nevertheless, less than 40% of the studies in our review assessed <i>a priori</i> generalizability. With the wide adoption of electronic health records systems, rich real-world patient databases are increasingly available for generalizability assessment; however, informatics tools are lacking to support the adoption of generalizability assessment practice.</span></span></span></p>
Job metrics for Big Data Technologies
<p>This dataset contains metrics for a 10 minute job running on YARN. More specifically it contains metrics for:</p> <ul> <li>YARN</li> <li>HDFS</li> <li>System Metrics (CPU, RAM, HDD, Interface etc.)</li> </ul> <p> </p> <p> </p>
Experimental results of "Managing variant calling datasets the big data way"
<p>Tomatula was demonstrated for retrieving the allele frequencies for a given region in the data from Aflitos et al (2014). We developed scripts to retrieve allele frequencies, either from the VCF file storage or Apache Parquet. We executed a series of experiments, querying for a region of 2000 bases in the file of chromosome 6, that corresponds to the approximate length of a gene. We compared both storage formats (VCF files and Parquet), two input sizes (104 and 1144 individuals), different cluster sizes varying between 2 and 150 executor nodes, and HDFS replication factor was set to 3, 5, 7, and 9, in order to examine four main factors that<br> can affect the performance of a Big Data cluster: (a) the storage format, (b) the size of the input files, (c) the number of computing nodes of the cluster, and (d) the replication factor of HDFS. The block size of the HDFS was kept at the default value of 128MB. All experiments were executed five times and the detailed results are provided here, along with a script that produces the corresponding figures.</p>
Automotive Sensor Data. An Example Dataset from the AEGIS Big Data Project
<p>This is an example research data dataset for the automotive demonstrator within the "AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security" big data project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732189. The time series data has been collected by using a BeagleBone single plate computer which has been developed at VIF to collect data for driving analytics. The BeagleBoard can be connected to the OBD2 interface of a vehicle to capture data from CAN bus and has been additionally equipped with further sensors (GPS, gyroscope, acceleration). The data in this research dataset was collected during 35 different trips conducted by one driver driving one vehicle in the Graz area in Austria.</p>
Big Data Analytics: Data-driven optimization of manufacturing companies
<p>This dataset contains the result of a survey conducted to identify the transformation of manufacturing companies by big data analytics. It contains 154 participations from employees of manufacturing companies of which 133 have completed all questions. The dataset is UTF8 encoded and available in German (original) as well as English (translated).</p>
Spatial Data Recovery and Resilience of Oyster Reefs in the Big Bend of Florida
<p><strong>Project:</strong> Recovery and Resilience of Oyster Reefs in the Big Bend of Florida</p> <p><a href="https://wec.ifas.ufl.edu/oysterproject/">https://wec.ifas.ufl.edu/oysterproject/</a></p> <p><strong>Lone Cabbage Reef Restoration Spatial Data (2017-2023) Repository:</strong></p> <p><a href="../communities/lonecabbagereef">https://zenodo.org/communities/lonecabbagereef</a></p> <p><strong>Contact:</strong> Joe Aufmuth, University of Florida, George A. Smathers Libraries, Academic Research and Consulting Services Department, <a href="mailto:mapper@ufl.edu">mapper@ufl.edu</a>, (352) 273-0371.</p> <p><strong>Clarifying Publication: </strong>Aufmuth, Moore, Pine, and Ennis (2024 in progress), An Oyster’s Pearl: Restoring the Elevation of Lone Cabbage Reef, Florida.</p> <p>The repository contains ArcGIS Map Packages (v3.2.0) that are listed in the repository file Descriptions_Lone_Cabbage_Reef_map_package_list_xls. </p> <p><strong>Purpose:</strong> Data collected varies in scale as well as positional and attribute accuracy. It is the responsibility of the user to verify that the data are appropriate for their project. No warranties or guarantees are made that the data are appropriate for uses other than the Recovery and Resilience of Oyster Reefs in the Big Bend of Florida project.</p> <p><strong>Data Collection:</strong> Elevation data was collected through professional certified surveyors (Lone Cabbage Reef 2017, 2018, and 2021) as well as through field data collection efforts using Trimble survey grade GPS equipment (University of Florida 2019). Oyster count data locations were collected through field efforts and mapped to field transects using Juniper GPS survey equipment (2018, 2019, 2020, 2021, 2022, 2023). Other spatial data layers included in this data set are credited in the layouts that produce the individual maps in the map packages.</p>
Big data analytics in support of the under-rail maintenance management at Vitória – Minas Railway
<p>This video describes an ongoing study using data collected by an instrumented ore car on Vitória–Minas Railway, operated by Vale in Brazil. The research uses big data analysis methods over collected data by the instrumented car during its voyages. Railway geometry issues can cause undesirable movements on the wagons that can cause discomfort for passengers or instability for the cargo. In the worst scenario, derailments can occur. Each second, several sensors installed on the instrumented car collect data about velocity, acceleration, and movements on the wagon. The volume of collected data is impressive since the railway has about 2,000 km of extension. That volume compels us to use big data analytics methods. As the result of the research, the team aims to establish some levels of operational conditions, named as severity indexes, which can indicate to the maintenance teams the necessity of intervention on the railway.</p>
Data for: Modeling of spatial pattern and influencing factors of cultivated land quality based on spatial-temporal big data (PONE-D-21-21084R1)
<p>The quality of cultivated land determines the production capacity of cultivated land and the level of regional development, and also directly affects the food security and ecological safety of the country. This paper starts from the perspective of spatial pattern of cultivated land quality and uses spatial autocorrelation analysis to study the spatial aggregation characteristics and differences of cultivated land quality in Henan Province at the county level scale, and also uses bivariate spatial autocorrelation to analyze the influence of neighboring influences on the quality of cultivated land in the target area. The spatial autoregressive model was used to further analyze the driving factors affecting the quality of cultivated land, and the influence of cultivated land area index was coupled in the process of rating analysis, which was finally used as a basis to propose more precise measures for the protection of cultivated land zoning. The results show that: (1) The quality of cultivated land in Henan Province has a strong spatial correlation (global Moran's I≈0.710) and shows an obvious aggregation pattern in spatial distribution; positive correlation types (high-high and low-low) are concentrated in north-central and western mountainous areas of Henan Province, respectively; negative correlation types are discrete. The negative correlation types are distributed in a discrete manner. (2) The bivariate spatial autocorrelation results show that Slope (Moran's I≈-0.505), Irrigation guarantee rate (IGR, 0.354), Urbanization rate (-0.255), Total agricultural machinery power (TAMP, 0.331) and Pesticide use (0.214) are the main influencing factors. (3) According to the absolute values of the regression coefficients, it can be seen that the magnitude of the influence of different factors on the quality of cultivated land is: Slope (0.089) >IGR (0.025) > Urbanization rate (0.002) > TAMP (0.001) > Pesticide use (1.96e-006). (4) Based on the spatial pattern presented by the spatial autocorrelation results, we proposed corresponding protection zoning measures to provide more scientific reference decisions and technical support for the implementation of refined cultivated land management in Henan Province. </p>
Data for NHESS manuscript by Biass et al. (2022): Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data
<p>This repository contains the data produced in the context of the following paper:</p> <blockquote> <p>Biass S, Jenkins SF, Aberhard WH, Delmelle P, Wilson T (2022): Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data, Accepted in NHESS</p> </blockquote> <p>Naming convention is: `run_date`_`landcover`_`impact_metrics`_`VI`_`anomaly`_test.pkl, where:</p> <ul> <li>Landcover is either crops, shrubs, herbaceous vegetation (grass), forests (trees) or all together</li> <li>Impact metrics is either minV (impact magnitude) or minT (impact duration)</li> <li>VI is the vegetation index (here, EVI)</li> <li>Anomaly is the impact indicator (here, cumulative difference index)</li> </ul> <p>Refer to the associated paper for more information on the methodology.</p> <p>Files are saved as .pkl and were generated by the <a href="https://explainerdashboard.readthedocs.io/en/latest/">explainerdashboard</a> library. They are the result of <a href="https://xgboost.readthedocs.io/en/stable/">XGBoost</a> runs that were optimised with <a href="https://optuna.org">Optuna</a> and analysed with the <a href="https://shap.readthedocs.io/en/latest/">SHAP</a> library. They contain:</p> <ol> <li>The explanatory variables and observed and computed target variables for all features</li> <li>The SHAP values</li> </ol> <p>To load the files, use <a href="https://explainerdashboard.readthedocs.io/en/latest/cli.html?highlight=load#explainerdashboard.explainers.BaseExplainer.from_file">this method</a>.</p> <p> </p>
DMP Examine the correlation between Twitter Sentiment data and the stock data of the 4 big tech companies Apple, Amazon, Google and Microsoft
<p><span>The purpose of using these specific datasets are, the possibilities they give to analyze the stock data changes, based on the sentiment analysis of the previous day. Including this output information it is possible to analyze our goal of searching for possible correlation between those two. </span></p>
On the Timed Analysis of Big-Data Applications - Experimental Data
<p>This archive includes experimental data associated to the paper:</p> <p>On the Timed Analysis of Big-Data Applications. Accepted in <em>Proceedings of Nasa Formal Methods</em> (NFM 2018). <br> Marconi, F., Quattrocchi, G., Baresi, L., Bersani, M.M., Rossi, M.. 2018</p> <p>Specifically, it includes detailed data regarding the verification tasks reported in Section 4 (Implementation and Validation of the Model).<br> In reference to Table 1 of the paper, the archive is organized in the following way: there is one folder for each case study (sort_by_key, pagerank, kmeans) and, for each of these folders, there is a subfolder for each configuration considered in the paper.</p> <p>Here we report an overview of the verification tasks performed. The name of the tables correspond to the code of the setting and to the name of the folder, while the id of each entry corresponds to the folder name of each experiment.</p> <pre><code class="language-markdown">## SortByKey Experiments ### sort_by_key_C12_T100_rec260000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 12 | 91000 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91000_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91100 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91100_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91200 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91200_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91300 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91300_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91360 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91360_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91370 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91370_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91380 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91380_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91381 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91381_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91382 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91382_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91383 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91383_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91384 | sat | 3.81 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91384_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91385 | sat | 3.34 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91385_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91386 | sat | 3.52 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91386_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91387 | sat | 3.42 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91387_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91388 | sat | 3.4 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91388_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91389 | sat | 3.43 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91389_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91390 | sat | 2.37 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91390_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91400 | sat | 3.38 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91400_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91500 | sat | 16.52 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91500_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91600 | sat | 6.8 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91600_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91700 | sat | 12.03 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91700_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91800 | sat | 5.62 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91800_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91900 | sat | 5.54 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91900_tc_12_8_n_rounds_by1_t_task | the minimum SAT deadline is: 91384 ### sort_by_key_C12_T100_rec280000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 12 | 98200 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98200_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98300 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98300_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98400 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98400_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98402 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98402_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98403 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98403_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98404 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98404_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98405 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98405_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98406 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98406_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98407 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98407_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98408 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98408_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98409 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98409_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98410 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98410_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98420 | sat | 3.48 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98420_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98430 | sat | 3.57 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98430_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98440 | sat | 6.68 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98440_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98450 | sat | 7.1 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98450_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98460 | sat | 37.07 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98460_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98470 | sat | 10.33 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98470_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98480 | sat | 18.14 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98480_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98490 | sat | 10.78 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98490_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98500 | sat | 3.43 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98500_tc_12_8_n_rounds_by1_t_task | the minimum SAT deadline is: 98420 ### sort_by_key_C12_T100_rec300000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 12 | 105200 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105200_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105300 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105300_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105400 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105400_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105420 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105420_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105430 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105430_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105440 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105440_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105441 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105441_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105442 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105442_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105443 | sat | 3.33 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105443_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105444 | sat | 3.35 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105444_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105445 | sat | 3.3 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105445_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105446 | sat | 3.31 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105446_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105447 | sat | 3.37 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105447_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105448 | sat | 3.37 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105448_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105449 | sat | 3.3 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105449_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105450 | sat | 3.02 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105450_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105460 | sat | 3.03 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105460_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105470 | sat | 4.52 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105470_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105480 | sat | 4.54 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105480_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105490 | sat | 9.48 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105490_tc_12_8_n_rounds_by1_t_task | the minimum SAT deadline is: 105443 ### sort_by_key_C22_T100_rec260000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 22 | 70000 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d70000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 70500 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d70500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 71000 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d71000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 71500 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d71500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72000 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72250 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72250_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72500 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72750 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72750_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72885 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72885_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72890 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72890_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72895 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72895_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72898 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72898_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72899 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72899_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72900 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72900_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72901 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72901_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72902 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72902_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72903 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72903_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72904 | sat | 9.35 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72904_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72905 | sat | 8.16 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72905_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72910 | sat | 8.15 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72910_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72915 | sat | 7.72 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72915_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72920 | sat | 7.55 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72920_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72925 | sat | 5.29 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72925_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72930 | sat | 10.23 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72930_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72935 | sat | 11.21 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72935_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72940 | sat | 10.98 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72940_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72945 | sat | 12.62 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72945_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72950 | sat | 12.55 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72950_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72955 | sat | 3.19 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72955_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72960 | sat | 2.62 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72960_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72965 | sat | 2.62 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72965_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72970 | sat | 2.63 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72970_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72975 | sat | 2.62 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72975_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72980 | sat | 2.72 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72980_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72985 | sat | 2.6 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72985_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72990 | sat | 2.55 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72990_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72995 | sat | 2.56 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72995_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 73000 | sat | 3.2 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d73000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 74000 | sat | 6.34 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d74000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 75000 | sat | 3.88 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d75000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 76000 | sat | 4.6 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d76000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 77000 | sat | 4.15 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d77000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78000 | sat | 1.89 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d78000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 79000 | sat | 3.28 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d79000_tc_22_10_n_rounds_by1_t_task | the minimum SAT deadline is: 72904 ### sort_by_key_C22_T100_rec280000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 22 | 78000 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78480 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78480_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78485 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78485_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78490 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78490_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78492 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78492_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78494 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78494_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78495 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78495_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78496 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78496_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78497 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78497_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78498 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78498_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78499 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78499_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78500 | sat | 40.11 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78500_tc_22_10_n_rounds_by1_t_task_num_v | | sort_by_key | 22 | 78750 | sat | 1.33 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78750_tc_22_10_n_rounds_by1_t_task_num_v | | sort_by_key | 22 | 79000 | sat | 9.14 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d79000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 80000 | sat | 2.27 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d80000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 81000 | sat | 1.98 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d81000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 82000 | sat | 5.89 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d82000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 83000 | sat | 8.81 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d83000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84000 | sat | 1.86 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d84000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 85000 | sat | 2.46 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d85000_tc_22_10_n_rounds_by1_t_task | the minimum SAT deadline is: 78500 ### sort_by_key_C22_T100_rec300000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 22 | 5000 | unsat | 0.56 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d5000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 10000 | unsat | 0.44 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d10000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 12000 | unsat | 1.07 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d12000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 15000 | unsat | 1.72 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d15000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 20000 | unsat | 1.13 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d20000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 25000 | unsat | 3.71 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d25000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 30000 | unsat | 4.27 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d30000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 35000 | unsat | 15.73 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d35000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 40000 | unsat | 8.82 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d40000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 45000 | unsat | 15.27 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d45000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 50000 | unsat | 19.42 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d50000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 55000 | unsat | 39.81 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d55000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 60000 | unsat | 39.71 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d60000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 65000 | unsat | 87.48 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d65000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 70000 | unsat | 120.21 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d70000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72500 | unsat | 172.18 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d72500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 74000 | unsat | 186.82 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d74000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 75000 | unsat | 120.49 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d75000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 76000 | unsat | 1175.84 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d76000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 76250 | unsat | 1167.23 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d76250_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 77500 | unsat | 2091.15 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d77500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78250 | unsat | 3000.66 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d78250_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 80000 | unsat | 34239.41 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d80000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 81250 | unsat | 79433.55 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d81250_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 82500 | unsat | 198577.62 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d82500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 83000 | unsat | 183847.9 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d83000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 83750 | unsat | 245288.49 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d83750_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84000 | unsat | 276617.22 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84100 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84100_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84101 | unsat | 253379.92 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84101_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84102 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84102_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84103 | unsat | 254808.3 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84103_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84104 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84104_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84105 | unsat | 269304.76 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84105_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84106 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84106_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84107 | unsat | 242278.6 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84107_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84108 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84108_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84109 | unsat | 260411.26 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84109_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84110 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84110_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84111 | unsat | 259311.3 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84111_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84112 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84112_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84113 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84113_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84114 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84114_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84115 | unsat | 230687.19 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84115_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84116 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84116_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84117 | unsat | 281773.79 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84117_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84118 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84118_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84119 | unsat | 272785.32 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84119_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84120 | sat | 4.09 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84120_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84121 | sat | 3.98 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84121_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84122 | sat | 4.01 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84122_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84123 | sat | 4.28 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84123_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84124 | sat | 4.24 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84124_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84125 | sat | 4.22 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84125_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84126 | sat | 4.25 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84126_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84127 | sat | 4.39 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84127_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84128 | sat | 4.23 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84128_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84129 | sat | 4.14 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84129_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84130 | sat | 4.07 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84130_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84131 | sat | 4.24 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84131_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84132 | sat | 4.42 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84132_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84133 | sat | 4.32 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84133_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84134 | sat | 4.31 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84134_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84135 | sat | 4.33 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84135_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84136 | sat | 4.26 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84136_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84137 | sat | 4.01 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84137_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84138 | sat | 3.82 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84138_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84139 | sat | 4.07 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84139_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84140 | sat | 4.38 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84140_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84141 | sat | 8.74 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84141_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84142 | sat | 8.98 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84142_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84143 | sat | 9.1 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84143_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84144 | sat | 9.09 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84144_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84145 | sat | 9.19 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84145_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84146 | sat | 9.1 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84146_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84147 | sat | 8.82 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84147_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84148 | sat | 6.54 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84148_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84149 | sat | 6.56 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84149_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84150 | sat | 6.59 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84150_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84151 | sat | 6.84 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84151_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84152 | sat | 6.87 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84152_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84153 | sat | 6.97 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84153_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84154 | sat | 6.88 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84154_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84155 | sat | 6.98 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84155_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84156 | sat | 6.71 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84156_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84157 | sat | 6.75 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84157_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84158 | sat | 6.42 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84158_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84159 | sat | 6.62 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84159_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84160 | sat | 6.75 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84160_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84161 | sat | 6.95 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84161_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84162 | sat | 7.03 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84162_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84163 | sat | 6.98 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84163_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84164 | sat | 7.08 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84164_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84165 | sat | 6.93 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84165_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84166 | sat | 6.81 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84166_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84167 | sat | 6.47 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84167_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84168 | sat | 6.66 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84168_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84169 | sat | 6.73 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84169_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84171 | sat | 6.8 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84171_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84172 | sat | 6.81 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84172_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84173 | sat | 6.71 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84173_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84174 | sat | 3.56 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84174_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84175 | sat | 3.26 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84175_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84176 | sat | 3.52 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84176_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84177 | sat | 3.6 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84177_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84178 | sat | 3.02 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84178_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84179 | sat | 3.6 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84179_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84180 | sat | 3.62 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84180_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84181 | sat | 3.59 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84181_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84182 | sat | 3.62 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84182_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84183 | sat | 6.72 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84183_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84184 | sat | 6.58 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84184_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84186 | sat | 6.59 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84186_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84187 | sat | 6.85 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84187_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84188 | sat | 6.56 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84188_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84189 | sat | 6.88 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84189_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84190 | sat | 6.84 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84190_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84191 | sat | 6.86 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84191_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84192 | sat | 6.94 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84192_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84193 | sat | 6.93 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84193_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84194 | sat | 6.91 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84194_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84195 | sat | 6.84 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84195_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84196 | sat | 6.99 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84196_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84197 | sat | 6.89 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84197_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84198 | sat | 6.95 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84198_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84200 | sat | 8.04 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84200_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84300 | sat | 11.06 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84300_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 </code></pre> <p> </p> <pre><code class="language-markdown"> ## PageRank Experiments ### pagerank_C28_T128_rec200000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 28 | 61000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d61000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 61500 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d61500_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 62000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d62000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 62500 | sat | 7805.0 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d62500_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 63000 | sat | 330.87 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d63000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 63500 | sat | 79.78 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d63500_tc_28_12_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 62500 ### pagerank_C28_T128_rec300000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 28 | 93000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d93000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 94000 | sat | 192.86 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d94000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 94001 | sat | 7331.63 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d94001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 94501 | sat | 1934.68 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d94501_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 94502 | sat | 2856.96 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb55_no_l_d94502_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 95000 | sat | 221.22 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d95000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 95001 | sat | 173.91 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d95001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 96001 | sat | 87.89 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d96001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 97001 | sat | 70.07 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d97001_tc_28_12_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 94000 ### pagerank_C28_T128_rec400000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 28 | 118000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d118000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 119000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d119000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 119001 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d119001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 120000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d120000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 120001 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d120001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 120002 | sat | 138.84 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d120002_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 120003 | sat | 272.19 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d120003_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 120004 | sat | 2072.06 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d120004_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 121000 | sat | 580.28 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d121000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 121001 | sat | 55.8 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d121001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 122000 | sat | 301.84 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d122000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 122001 | sat | 187.93 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d122001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 124000 | sat | 51.47 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d124000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 131000 | sat | 46.01 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d131000_tc_28_12_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 120002 ### pagerank_C48_T128_rec200000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 48 | 5000 | unsat | 20.91 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d5000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 10000 | unsat | 1002.53 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d10000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 15000 | unsat | 407.96 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d15000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 20000 | unsat | 667.32 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d20000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 25000 | unsat | 1263.14 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d25000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 30000 | unsat | 3561.83 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d30000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 35000 | unsat | 12742.87 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d35000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 37000 | unsat | 44051.41 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d37000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 40000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d40000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 43000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d43000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 45000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d45000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46050 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46050_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46100 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46100_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46200 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46200_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46250 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46250_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46750 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46750_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46900 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46900_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 47000 | sat | 59.41 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d47000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 48000 | sat | 39.88 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d48000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 49000 | sat | 31.35 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d49000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 50000 | sat | 45.02 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d50000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 51000 | sat | 43.73 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d51000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 52000 | sat | 36.25 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d52000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 52500 | sat | 40.5 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d52500_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 53500 | sat | 40.61 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d53500_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 54000 | sat | 39.9 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d54000_tc_48_16_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 47000 ### pagerank_C48_T128_rec300000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 48 | 64000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d64000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65050 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65050_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65100 | sat | 1282.46 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65100_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65200 | sat | 102.8 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65200_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65250 | sat | 75.45 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65250_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65500 | sat | 39.15 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65500_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65750 | sat | 78.42 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65750_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65900 | sat | 33.61 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65900_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 66000 | sat | 96.91 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d66000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 67000 | sat | 56.04 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d67000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 68000 | sat | 65.88 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d68000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 69000 | sat | 40.79 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d69000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 70000 | sat | 51.82 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d70000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 72000 | sat | 49.1 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d72000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 73000 | sat | 26.86 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d73000_tc_48_16_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 65100 ### pagerank_C48_T128_rec400000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 48 | 85000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d85000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 86000 | sat | 278.68 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d86000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 87000 | sat | 55.3 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d87000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 88000 | sat | 55.59 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d88000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 89000 | sat | 42.06 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d89000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 90000 | sat | 51.77 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d90000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 91000 | sat | 45.12 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d91000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 92000 | sat | 38.55 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d92000_tc_48_16_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 86000 </code></pre> <p> </p> <pre><code class="language-markdown">## K-Means Experiments ### kmeans_C24_T18_rec80000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 24 | 79000 | 50 | sat | 61854.62 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d79000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 79500 | 50 | sat | 18414.51 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d79500_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 80000 | 50 | sat | 14840.75 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d80000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 85000 | 50 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d85000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 90000 | 50 | sat | 14590.33 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d90000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 100000 | 50 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d100000_tc_24_6_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 79000 ### kmeans_C24_T18_rec120000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 24 | 105000 | 50 | sat | 152363.81 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d105000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 105000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb80_no_l_d105000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 106000 | 50 | sat | 43759.32 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d106000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 106500 | 50 | sat | 22230.43 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d106500_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 107000 | 50 | sat | 25027.5 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d107000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 108000 | 50 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d108000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 110000 | 50 | sat | 40917.73 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d110000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 110000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb80_no_l_d110000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 115000 | 50 | sat | 12142.65 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d115000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 115000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb80_no_l_d115000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 120000 | 50 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d120000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 120000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb80_no_l_d120000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 50 | sat | 14259.95 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 60 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb60_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 70 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb70_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 80 | sat | 14189.62 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb80_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 90 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb90_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 105000 ### kmeans_C24_T18_rec160000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 24 | 140000 | 50 | sat | 40930.77 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr160000000_tb50_no_l_d140000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 140000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr160000000_tb80_no_l_d140000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 50 | sat | 21741.22 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr160000000_tb50_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr160000000_tb80_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 140000 ### kmeans_C32_T24_rec80000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 32 | 63000 | 80 | sat | 26400.12 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr80000000_tb80_no_l_d63000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 65000 | 80 | sat | 10414.91 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr80000000_tb80_no_l_d65000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 70000 | 80 | sat | 5554.03 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr80000000_tb80_no_l_d70000_tc_32_8_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 63000 ### kmeans_C32_T24_rec120000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 32 | 82000 | 60 | sat | 2692.91 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb60_no_l_d82000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 82000 | 100 | running | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb100_no_l_d82000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 83000 | 60 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb60_no_l_d83000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 83000 | 100 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb100_no_l_d83000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 90000 | 60 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb60_no_l_d90000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 90000 | 100 | sat | 60535.12 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb100_no_l_d90000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 50 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb50_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 60 | sat | 1240.52 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb60_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 70 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb70_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 80 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb80_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 90 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb90_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 100 | sat | 1957.42 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb100_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 82000 ### kmeans_C32_T24_rec160000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 32 | 103000 | 50 | sat | 47624.75 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb50_no_l_d103000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 103000 | 80 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb80_no_l_d103000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 105000 | 50 | sat | 38614.93 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb50_no_l_d105000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 105000 | 80 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb80_no_l_d105000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 110000 | 50 | sat | 11604.73 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb50_no_l_d110000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 120000 | 50 | sat | 11559.37 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb50_no_l_d120000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 120000 | 80 | sat | 27413.19 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb80_no_l_d120000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 50 | sat | 1811.52 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb50_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 60 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb60_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 70 | sat | 2423.68 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb70_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 80 | sat | 952.42 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb80_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 90 | sat | 1300.52 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb90_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 100 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb100_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 103000 </code></pre> <p> </p>
NEPHELE big data experiment
<p>The big data experiments use HiBench for benchmarking, which generates data using a number of data generators. These generators generate text data, web-data that follows a Zipfian distribution, numeric data for k-means clustering following a uniform or Gaussian distribution. Each of those benchmark instances consists of ten micro benchmarks. Some of those micro benchmarks provide multiple implementations. These differ in language of implementation and framework. The frameworks includes Hadoop and Spark. The Spark-based implementations feature code in Java, Python, and Scala. Below is a list of evaluated workloads and data:</p> <p>1. Sort – This micro benchmark sorts text data, which is generated by RandomTextWriter.</p> <p>2. WordCount – This micro-benchmark counts the occurrences of words in the input data, which is generated by RandomTextWriter.</p> <p>3. TeraSort – This micro benchmark sorts a large data set generated by TeraGen. This micro benchmark has been used to push the boundaries of sorting very large data sets.</p> <p>4. Sleep – This micro benchmark sleeps for a set amount of time to test the scheduler.</p> <p>5. SQL (Scan, Join, Aggregate) – This benchmark tests typical SQL commands using Hive queries. The benchmark operates on generated hyperlinks.</p> <p>6. PageRank – This benchmark computes the PageRank on generated hyperlinks.</p> <p>7. Nutch indexing – Nutch is an Apache search engine and implements an indexing algorithm. The benchmark operates on generated web sites with words and hyperlinks.</p> <p>8. Bayesian classification – This benchmarks uses Spark MLLib and Mahout to perform Naive Bayesian classification. The data consists of generated text documents.</p> <p>9. K-means clustering – In this benchmark the k-means clustering algorithm in Spark MLLib/Mahout is used to cluster generated input data. </p> <p>10. Enhanced DFSIO – This benchmark tests the HDFS throughput by generating a large number of tasks that simultaneously write and read data from HDFS.</p>
Optimizing Quality-Aware Big Data Applications in the Cloud — Supporting Data
<p>Supporting experimental data for the paper "Optimizing Quality-Aware Big Data Applications in the Cloud", submitted to IEEE TCC.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.